Adaptive equalizer with a dual-mode active taps mask generator and a pilot reference signal amplitude control unit

ABSTRACT

An adaptive equalizer including an equalizer filter and a tap coefficients generator used to process a sample data stream derived from a plurality of received signals is disclosed. The tap coefficients generator includes an equalizer tap update unit, a vector norm square estimator, an active taps mask generator, a switch and a pilot amplitude reference unit used to minimize the dynamic range of the equalizer filter. A dynamic mask vector is used to mask active taps generated by the equalizer tap update unit when an unmasked signal output by the equalizer filter is selected by the switch to generate an error signal fed to the equalizer tap update unit. A fixed mask vector is used to mask active taps generated by the equalizer tap update unit when a masked signal output by the equalizer filter is used to generate the error signal.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.11/265,373, filed Nov. 2, 2005, which claims the benefit of U.S.Provisional Patent Application No. 60/625,188, filed Nov. 5, 2004, whichis incorporated by reference as if fully set forth.

FIELD OF INVENTION

The present invention is related to an adaptive equalizer used in anormalized least mean square (NLMS) chip-level equalization (CLE)receiver. More particularly, the present invention relates to a pilotamplitude reference unit which controls the output power of the adaptiveequalizer, and an active taps reference unit which generates an activetaps mask when either a static filter tap masking mode or a dynamicfilter tap masking mode is implemented.

BACKGROUND

An adaptive equalizer based receiver, such as an NLMS-based receiver,provides superior performance for high data rate services such asfrequency division duplex (FDD) high speed downlink packet access(HSDPA) or code division multiple access (CDMA) 2000 evolution datavoice (EV-DV) over a Rake receiver. A typical NLMS receiver includes anequalizer having an equalizer filter and a tap coefficients generator.The equalizer filter is typically a finite impulse response (FIR)filter. The tap coefficients generator in the equalizer generatesappropriate tap coefficients for the equalizer filter and uses an NLMSalgorithm to update the tap coefficients appropriately and iterativelyin a timely basis. The NLMS algorithm attempts to converge to a minimummean square error (MMSE) solution by iteratively updating the tapcoefficient weights such that, on average, they approach the MMSEsolution.

Typically, an error signal computation, a vector norm calculation andleaky integration is required to generate and update the tapcoefficients. When the optimal equalizer filter tap coefficients containone or more zero values, it would be desirable to effectively removesome of the taps from the equalizer filter by masking the taps, ratherthan having the NLMS algorithm try to set the tap values to zero. TheNLMS algorithm can only make the tap values small since there is alwayssome noise perturbing the system and because step sizes cannot be madesmall in time varying channels. By masking the taps, the overallperformance of the adaptive equalizer based receiver would be improved,especially when small delay spread channels or sparse channels areencountered.

SUMMARY

The present invention is related to an adaptive NLMS CLE receiver whichincludes an adaptive equalizer having an equalizer filter and a tapcoefficients generator used to process a sample data stream derived froma plurality of received signals. The tap coefficients generator includesan equalizer tap update unit, a vector norm square estimator, an activetaps mask generator, a switch and a pilot amplitude reference unit usedto minimize the dynamic range of the equalizer filter. A dynamic maskvector is used to mask active taps generated by the equalizer tap updateunit when an unmasked signal output by the equalizer filter is selectedby the switch to generate an error signal fed to the equalizer tapupdate unit. A fixed mask vector is used to mask active taps generatedby the equalizer tap update unit when a masked signal output by theequalizer filter is used to generate the error signal.

BRIEF DESCRIPTION OF THE DRAWINGS

A more detailed understanding of the invention may be had from thefollowing description, given by way of example and to be understood inconjunction with the accompanying drawings wherein:

FIG. 1 is a high-level block diagram of an exemplary NLMS CLE receiverconfigured in accordance with one embodiment of the present invention;

FIG. 2 is a block diagram of a baseband frequency correction (BFC) unitincluding a frequency error estimator used to remove residual automaticfrequency control (AFC) errors in the NLMS CLE receiver of FIG. 1;

FIG. 3 is an exemplary block diagram of a frequency error estimator usedin the BFC unit of FIG. 2;

FIG. 4 is an exemplary block diagram of a step-size estimator includingan apparent channel speed estimator used in the receiver of FIG. 1;

FIG. 5 is a high-level block diagram depicting the integration of anactive taps mask generator within the NLMS CLE receiver of FIG. 1;

FIG. 6 is a detailed block diagram of the active taps mask generator ofFIG. 5;

FIG. 7 is a detailed block diagram depicting the integration of a pilotamplitude reference unit in the NLMS CLE receiver of FIG. 1;

FIG. 8 is a high-level block diagram of an exemplary NLMS CLE receiverconfigured in accordance with another embodiment of the presentinvention; and

FIG. 9 is a detailed block diagram depicting the integration of a pilotamplitude reference unit in the NLMS CLE receiver of FIG. 8.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The preferred embodiments will be described with reference to thedrawing figures where like numerals represent like elements throughout.

When referred to hereafter, the terminology “wireless transmit/receiveunit” (WTRU) includes but is not limited to a user equipment (UE), amobile station, a fixed or mobile subscriber unit, a pager, or any othertype of device capable of operating in a wireless environment.

When referred to hereafter, the terminology “transceiver” includes butis not limited to a base station, a WTRU, a Node-B, an access point (AP)or any other wireless communication device that receives signals fromand transmits signals to another transceiver.

When referred to hereafter, the terminology “apparent channel speed” and“apparent speed of a channel” includes but is not limited to theobserved and/or measured rate of change of an impulse response of achannel established between a first transceiver (e.g., WTRU, basestation, or the like), and at least one other transceiver. The change ofthe channel impulse response may be caused by the movement of one ormore of the transceivers, oscillator error which occur in at least oneof the transceivers, and the movement of objects in the environment inwhich at least one of the transceivers operates.

The features of the present invention may be incorporated into anintegrated circuit (IC) or be configured in a circuit comprising amultitude of interconnecting components.

Hereafter, the present invention will be explained with reference tomethods of receiver diversity for an NLMS algorithm. However, the NLMSalgorithm is used an example, and any other adaptive equalization orfiltering algorithm, such as least mean square (LMS), Griffith'salgorithm, channel estimation based NLMS (CE-NLMS), and other iterativeor recursive algorithms may be used.

FIG. 1 is a high-level block diagram of an exemplary NLMS CLE receiver100 configured in accordance with the present invention. The NLMS CLEreceiver 100 is a joint processing NLMS receiver which uses a singleadaptive equalizer filter 120. The NLMS CLE receiver 100 includes aplurality of antennas 102A, 102B, a plurality of samplers 104A, 104B, amultiplexer 108, a multiplier 114 and an NLMS equalizer 118. The NLMSequalizer 118 includes the equalizer filter 120 and a tap coefficientsgenerator 122.

As shown in FIG. 1, signals received by the antennas 102A, 102B arerespectively input into the samplers 104A, 104B for generatingrespective sample data streams 106A, 106B which are sampled at two times(2×) the chip rate. The sample data streams 106A, 106B are merged by themultiplexer 108 into a single sample data stream 110 which is fed to afirst input of the multiplier 114. Since samples occur at twice the chiprate on each of the sample data streams 106A, 106B, samples will occurat 4 times (4×) the chip rate on the sample data stream 110. Each samplethat occurs on the sample data stream 110 originated from either sampledata stream 106A or 106B. The effective rate of the equalizer filter 120is four times (4×) the chip rate.

Although FIG. 1 illustrates the NLMS CLE receiver 100 as being capableof sampling signals received from two (2) antennas at twice (2×) thechip rate, it should be noted that the NLMS CLE receiver 100 maycomprise any number of antennas and the signals received by the antennasmay be sampled at any desired rate.

The equalizer filter 120 of the NLMS equalizer 118 comprises a pluralityof taps with filter coefficients. A FIR filter may be utilized as theequalizer filter 120. The number of taps in the equalizer filter 120 maybe optimized for specific multipath channels of different power-delayprofiles and vehicle speeds. The tap coefficients generator 122 includesa vector norm square estimator 132, an equalizer tap update unit 134, astep-size estimator 136, a BFC unit 138, an active taps mask generator140, a pilot amplitude reference unit 142, a switch 147, multipliers123, 124, 126, 128, and an adder 130.

The BFC unit outputs a rotating phasor which is fed to a second input ofthe multiplier 114 to correct the frequency of the sample data stream110, which will be explained in detail below with reference to FIGS. 2and 3. The multiplier 114 generates a frequency corrected sample datastream 116 which is fed to an input of the equalizer filter 120 in theNLMS equalizer 118.

Still referring to FIG. 1, the equalizer filter 120 outputs a maskedequalizer output (masked_eq_out) signal 144 which is provided whenactive tap masking is implemented, an unmasked equalizer output,(unmasked_eq_out), signal 146 which is provided when active tap maskingis not implemented, and an equalizer tapped delay line (TDL),(TDL_joint_vec_out), signal 148 which is always provided.

The masked equalizer output signal 144 is a chip rate signal that ismultiplied with a scrambling code conjugate, (scrambling_code_conj),signal 150 via the multiplier 124 to generate a descrambled maskedequalizer output signal 152 (i.e., an estimate of the unscrambledtransmitted chips), which is fed to a first input of the switch 147. Theunmasked equalizer output signal 146 is multiplied with the scramblingcode conjugate signal 150 via the multiplier 123 to generate adescrambled unmasked equalizer output error signal 154 which is fed to asecond input of the switch 147.

The equalizer TDL signal 148 is multiplied with the scrambling codeconjugate signal 150 via the multiplier 126 to generate a vector signal156 having a value X (i.e., a descrambled equalizer TDL signal). Thevector signal 156 is input to the vector norm square estimator 132 andto a first input of the equalizer tap update unit 134. The vector normsquare estimator 132 generates a vector normalization signal 158. Thevector norm square estimator 132 feeds the vector normalization signal158 to a second input of the equalizer tap update unit 134 and to thepilot amplitude reference unit 142.

Still referring to FIG. 1, when the active taps mask generator 140 is ina dynamic filter tap masking mode, the vector norm square estimator 132generates a vector normalization signal 158 having a value which isequal to the norm squared of the value X of the vector signal 156, ∥X∥²,or equivalently the equalizer TDL signal 148). When the active taps maskgenerator 140 is in a static filter tap masking mode, the vector normsquare estimator 132 generates a vector normalization signal 158 havinga value which is equal to the norm of the value X of the vector signal156 element-wise multiplied by M, ∥X×M∥², where M is an active tapsmask.

A masking mode signal 164 is fed to the active taps mask generator, theswitch 147 and the vector norm square estimator 132. The masking modesignal 164 indicates whether the dynamic or static filter tap maskingmode is being used. When the masking mode signal 164 indicates that thestatic filter tap masking mode is being used, the switch 147 selectssignal 152 as a selected output signal 166 to be fed to a first input ofthe adder 130. When the masking mode signal 164 indicates that thedynamic filter tap masking mode is being used, the switch 147 selectssignal 154 as the selected output signal 166. The configuration of theactive taps mask generator 140 is described in further detail below withrespect to FIGS. 5 and 6.

A pilot reference amplitude signal 168 generated by the pilot amplitudereference unit 142 is used to adjust the average output power of theNLMS equalizer 118 by changing the amplitude of a pilot reference signal172, which is generated by the multiplier 128 multiplying the pilotreference amplitude signal 168 with a scaled pilot (i.e., common pilotchannel (CPICH)), channelization code 170. The pilot reference amplitudesignal 168 is derived based on the vector normalization signal 158, theequalizer filter tap coefficients 162 and a power target signal 176. Thepilot reference signal 172 is input to a second input of the adder 130.The pilot amplitude reference unit 142 is further described in furtherdetail below with reference to FIG. 7.

The selected output signal 166 is subtracted from the pilot referencesignal 172 by the adder 130 to generate an error signal 174 which isinput to a third input of the equalizer tap update unit 134. Theexternal signals 150 and 170 are configured and generated based oninformation signaled from higher layers.

Based on the signals 156, 158, 135, 137, and 174, the equalizer tapupdate unit 134 generates equalizer filter tap coefficients 162 whichare input to the equalizer filter 120, the step-size estimator 136, theBFC unit 138, the active taps mask generator 140 and the pilot amplitudereference unit 142.

Based on the equalizer filter tap coefficients 162, the active taps maskgenerator 140 generates an active taps mask vector 160 which is fed tothe vector norm square estimator 132 and the equalizer filter 120.

The equalizer filter tap coefficients 162 represent the tap values usedby the equalizer filter 120. At a given time, the equalizer filter tapcoefficients 162 are computed based on the current value of theequalizer filter tap coefficients 162, the vector signal 156, the vectornormalized signal 158, the error signal 174, and a step-size, μ (“mu”),parameter 135 and filter taps leakage factor, α (“alpha”), parameter 137provided by the step-size estimator 136 based on a CPICH signal-to-noiseratio (SNR) input 139 which will be explained in detail below withreference to FIG. 4. A more detailed description of updating theequalizer filter tap coefficients 162 is provided below.

The error signal 174 is either based on the descrambled masked equalizeroutput signal 152 or the descrambled unmasked equalizer output signal154. The descrambled masked equalizer output signal 152 is used as theselected output signal 166 when the active taps algorithm in the activetaps mask generator 140 is not dynamically updating the active taps mask(i.e., the static filter tap masking mode). The descrambled unmaskedequalizer output signal 154 is used as the selected output signal 166when the active taps algorithm in the active taps mask generator 140 isdynamically updating the taps mask. During the dynamic filter tapmasking mode of operation, the active taps algorithm makes decisions onwhich taps to mask based on the values of the taps. If the descrambledmasked equalizer output signal 152 were to be used instead of thedescrambled unmasked signal 154 to generate the error signal 174, therewould be no feedback mechanism in the active taps algorithm to properlydrive the values of the masked taps. Therefore, the active tapsalgorithm would not function properly. Conversely, during the staticfilter tap masking mode of operation, the active taps algorithm does notmake any changes to the mask so it is insignificant whether the behaviorof the masked taps is correct. Thus, it is desired to use the errorsignal 174 based on the masked equalizer output signal 154 because theequalizer filter tap coefficients 162 will be optimized for generatingthe taps used to provide the equalizer output signal (i.e., signal 152).

The equalizer filter tap coefficients 162 are updated by the equalizertap update unit 134 as follows:

$\begin{matrix}{{{\overset{\rightarrow}{w}}_{n} = {{\alpha \cdot {\overset{\rightarrow}{w}}_{n - 1}} + {\mu {\frac{{\overset{\rightarrow}{x}}^{H}}{{\overset{\rightarrow}{x}}^{2} + ɛ} \cdot {error}}}}},} & {{Equation}\mspace{14mu} (1)}\end{matrix}$

where {right arrow over (w)}_(n) is a weight vector defined for theequalizer filter 120, n is an update or time index, {right arrow over(x)}, {right arrow over (x)}_(n) ^(H) are vectors based on the samplesreceived from the antennas 102A, 102B, μ, α, ε are parameters chosen tocontrol the adaptation step-size, tap leakage, and to prevent divisionby zero (or near zero) numbers respectively. ε is a small number used toprevent from dividing by zero. The leakage parameter α (alpha) is aweighting parameter, where 0<α≦1. The step-size parameter μ is a scalefactor on the error. The equalizer filter 120 is simply a FIR structurethat computes the inner product of {right arrow over (w)}_(n) and {rightarrow over (x)}, <{right arrow over (w)}_(n), {right arrow over (x)}>.The result of the inner product is the unmasked equalizer output signal146. The equalizer filter 120 also generates another masked equalizeroutput signal 144 that includes a mask M when the active taps maskgenerator 140 is in a static filter tap masking mode. The maskedequalizer output signal 144 is computed by first taking the element-wiseproduct of either {right arrow over (w)}_(n) or {right arrow over (x)}and then taking the inner product, <w,X*M>, where w is a particularweight, X is a particular received sample and M is an active taps maskincluded in the active taps mask vector generated by the active tapsmask generator 140. The present invention implements receive diversityin conjunction with an adaptive equalizer, which greatly improves thereceiver performance. A joint equalizer filter coefficient vectoradaptation scheme in accordance with the present invention is describedbelow. The joint equalizer is formulated in a context without anact_taps mask for clarity. However, that masking may be included incombination with receiver diversity.

A joint weight vector {right arrow over (w)}_(n,joint) is defined forthe equalizer filter as a union of multiple component weight vectors.Each component weight vector corresponds to data collected by adifferent antenna. Any permutation of elements from component vectorsmay comprise the joint weight vector so long as the permutation properlyreflects the order in which data enters the joint NLMS equalizer. Asthese are mathematically equivalent, the permutation may be chosen fornotational convenience. For example, for two antennas, the joint weightvector {right arrow over (w)}_(n,joint) can be defined as follows:

{right arrow over (w)}_(n,joint)=[{right arrow over (w)}_(n,1)^(T),{right arrow over (w)}_(n,2) ^(T)]^(T),  Equation (2)

where ( )^(T) denotes a transpose operation. The total number of taps ofthe equalizer filter is denoted by L. {right arrow over (w)}_(n,joint)is a column vector.

For the chosen notation in Equation (2), the notation for the jointupdate vector {right arrow over (x)}_(n,joint) is defined as follows:

{right arrow over (x)}_(n,joint)=[{right arrow over (x)}_(n) ¹,{rightarrow over (x)}_(n) ²],  Equation (3)

where {right arrow over (x)}_(n) ¹,{right arrow over (x)}_(n) ² arevectors based on the samples received from antenna 1 and antenna 2,respectively. {right arrow over (x)}_(n,joint) is a row vector.

The filter coefficient adaptation for the joint NLMS equalizer can thenbe processed in a usual way for an NLMS equalizer. For example, theupdated coefficient vector can be obtained as follows:

$\begin{matrix}{{{\overset{\rightarrow}{w}}_{{n + 1},\; {joint}} = {{\alpha \cdot {\overset{\rightarrow}{w}}_{n,\; {joint}}} + {\mu \frac{{\overset{\rightarrow}{x}}_{n,{joint}}^{H}}{{{\overset{\rightarrow}{x}}_{n,{joint}}}^{2} + ɛ}\left( {{d\lbrack n\rbrack} - {{\overset{\rightarrow}{x}}_{n,{joint}}{\overset{\rightarrow}{w}}_{n,{joint}}}} \right)}}},} & {{Equation}\mspace{14mu} (4)}\end{matrix}$

where ( )^(H) denotes a transpose conjugate operation, d[n] is thereference signal for NLMS and ε is a small number used to prevent fromdividing by zero. The parameter α is a weighting parameter and μ is ascale factor of error signal. The μ can be estimated based on thechannel speed and signal-to-interference and noise ratio (SINR) andinterpolated to obtain a continuous estimation.

For pilot-directed NLMS, d[n] can be a pilot signal, training signal, orother known pattern signals, either despread with pre-determineddespreading factors or non-despread. Similarly for data-directed NLMS,d[n] can be fully-, partially- or non-despread data symbols. The tapcorrection terms {right arrow over (Δ)}_(n) are computed as follows:

$\begin{matrix}{{{\overset{\rightarrow}{\Delta}}_{n} = {\mu {\frac{{\overset{\rightarrow}{x}}_{n,{joint}}^{H}}{{{\overset{\rightarrow}{x}}_{n,{joint}}}^{2} + ɛ} \cdot _{n,{joint}}}}},} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

where the factor e_(n,joint) is a joint error signal and is computed bysubtracting the equalizer filter output from the reference signal d[n]as follows:

e _(n,joint) =d[n]−{right arrow over (x)} _(n,joint) {right arrow over(w)} _(n,joint).  Equation (6)

The new tap coefficients for the next iteration are obtained by addingthe tap correction terms {right arrow over (Δ)}_(n) to the (possiblyweighted to provide leakage) tap coefficients of the previous iteration.The weighting mechanism can be characterized by a parameter α (alpha)formulated as follows:

{right arrow over (w)} _(n+1) =α·{right arrow over (w)} _(n)+{rightarrow over (Δ)}_(n).  Equation (7)

FIG. 2 is a block diagram of the BFC unit 138 used to remove residualautomatic frequency control (AFC) errors in the NLMS CLE receiver 100 ofFIG. 1. The BFC unit 138 includes a frequency error estimator 206, acontroller 208 and a numerically controlled oscillator (NCO) 210. Theequalizer filter 120 in the NLMS equalizer 118 of the NLMS CLE receiver100 of FIG. 1 processes the sample data stream 110 via the multiplier114. The equalizer filter tap coefficients 162 used by the equalizerfilter 120 are provided as an input to the frequency error estimator206. The frequency error estimator 206 generates an estimated frequencyerror signal 216. The residual frequency errors after AFC can be greatlyreduced by BFC based solely on observation of at least one tap value inthe equalizer filter 120, a combination of several tap values (e.g, asum), or alternatively from partial channel estimates, such as a Rakefinger complex weight estimation. BFC is accomplished by estimating thefrequency error based on observation of the one or more taps in theequalizer filter 120, generating a correction signal consisting of acomplex sinusoid (or rotating phasor), correcting the input samples datastream by multiplying it by the phasor and applying frequency correctedsamples 116 to the input of the equalizer filter 120 in a closed loopfashion.

The residual frequency error is estimated by periodically measuring thephase change of one or more of the tap values of the equalizer filter120 (or alternatively, partial channel estimates). Much of the phasechange measured on the equalizer filter tap coefficients 162 from sampleto sample is due to noise and fading. However, phase changes due tofading and noise are zero mean (e.g., have a mean value of zero). Thus,filtering can be used to reduce the noise and fading components of phasechanges from the overall phase changes, and to recover the slowlyvarying phase change due to the frequency error (residual AFC errors).

Once the frequency error is estimated by the frequency error estimator206, the controller 208 processes the estimated frequency error signal216 to generate a frequency adjustment signal 220. The controller 208may simply provide a gain to the estimated frequency error signal 216 ormay process the estimated frequency error signal 216 with a morecomplicated algorithm (e.g., a proportional-integral-derivative (PID)).The frequency adjustment signal 220 is fed to the NCO 210 whichgenerates a rotating phasor 112. The multiplier 114 multiplies therotating phasor 112 with the sample data stream 110 to generate thefrequency corrected samples 116 input into the equalizer filter 120.

Residual AFC errors manifest themselves in the baseband as amultiplicative error in the baseband signal and has the form of acomplex sinusoid, such as g(t)*exp(j*2pi*f*t) where g(t) is the desireduncorrupted baseband signal and exp(j*2pi*f*t) is the complex sinusoidrepresenting the error. By multiplying by exp(−j*2pi*f*t), the complexsinusoids cancel leaving only the desired signal g(t). The estimatedfrequency error signal 216 is input to the controller 208 which, inturn, outputs a signal 220 which may be, for example, a scaled (i.e.,proportional) version of the input, e.g., four (4) times the value ofthe estimated frequency error signal 216. The output signal 220 of thecontroller 208 may also include other terms such as a term proportionalto the integrals and/or derivatives of the estimated frequency errorsignal 216. More generally, the output signal 220 could also be clippedto be within some range or have other such non-linear function appliedto it. The NCO 210 takes as an input a frequency value and outputs aconstant magnitude complex signal with instantaneous frequency equal tothe value of the input, e.g., exp(j*2pi*f*t), where f is the inputfrequency.

FIG. 3 is a block diagram of the frequency error estimator 206 used inthe BFC unit of FIG. 2. The frequency error estimator 206 includes a tapextraction unit 302, a delay unit 304, a conjugate generator 306,multipliers 308, 310, an arctangent unit 312, a magnitude detector 314,an averaging filter 316, a phase change filter 318 and a comparator 320.The equalizer tap update unit 134 in the NLMS equalizer 118 generatesequalizer filter tap coefficients 162 which are supplied to thefrequency error estimator 206.

In the frequency error estimator 206, the tap extraction unit 302extracts and outputs an appropriate tap value or average of tap valuesonto an output signal 303 from the equalizer filter tap coefficients 162(or alternatively, from a channel estimator) to use for performingfrequency estimation. For example, at least one appropriate tap valuecorresponding to a first significant path (FSP) in a particular channelmay be extracted from the equalizer filter tap coefficients 162. The tapextraction unit 302 may also track drifting of a large valued tap andselect this tap as the extracted tap value.

The extracted tap value 303 is forwarded to the delay unit 304 and theconjugate unit 206. The delay unit 304 delays the extracted tap value303 for a predetermined period of time by outputting a delayed tap valueon 305. The conjugate generator is used to generate a conjugate 307 ofthe extracted tap value 303. The multiplier 308 multiplies the delayedtap value 305 by the conjugate tap value 307. The output 309 of themultiplier 308 has a phase value equal to the phase difference betweenthe delayed tap value 305 and the conjugate tap value 307. This phasevalue is proportional to the average frequency of the signal 303 andtherefore of the sample data stream 110.

The arctangent unit 312 measures an angle value 313 of the output 309 ofthe multiplier 308. The angle value 313 is equal to the phase differencebetween signal 305 and signal 307. Averaging the angle value 313 istherefore equivalent to averaging the phase difference between signal305 and signal 307. The angle value 313 is filtered by the phase changefilter 318 for averaging the angle value 313. The measured average phasedifference and the known delay are used to generate the estimatedfrequency error signal 316.

For example, with a delay D (sec) and phase measured in radians, thegain of the frequency error estimator 206 is 1/(2*PI*D). The “gain”refers to the conversion of a signal with a net frequency error (asindicated by signal 110) to an observed value of the estimated frequencyerror signal 216. If the signal 110 has an average frequency of 1 Hz,then the output value on the estimated frequency error signal 216 willbe 1(2*PI*D).

The magnitude detection unit 314 calculates the magnitude of the output309 of the multiplier 308 and sends a calculated magnitude value 315 toa first input, X, of the comparator 320 and to the averaging filter 316for averaging. The multiplier 310 multiplies the output signal 317 ofthe averaging filter 316 (i.e., the average value of signal 315) with athreshold factor value 319 (e.g., a scaling factor having a value T) togenerate a threshold signal 322 which is sent to a second input, Y, ofthe comparator 320. The value of the threshold signal 322 may be set toa fraction of the average amplitude of the output 309 of the multiplier308. The threshold factor value, T, may be set, for example, to ⅓. Thecomparator 320 compares the calculated magnitude value 315 with thevalue of the threshold signal 322 and sends a hold signal 321 to thephase change filter 318 if the calculated magnitude value 315 is belowthe value of the threshold signal 322.

The magnitude of the output 309 of the multiplier 308 may be measuredand compared to a fraction of the average amplitude of the output 309 ofthe multiplier 308, whereby the phase change filter 318 is pausedwhenever the magnitude of the output 309 of the multiplier 308 dropsbelow a threshold. When the filter 318 is paused, the estimatedfrequency error signal 216 does not change (i.e., the signal 216 is notupdated), the input 313 is not used, and the internal state of thefilter 318 does not change. The hold signal 321 is true whenever thesignal 309 is relatively small. This has the effect of discarding theangle values on signal 313 whenever they are noisiest, and improving theestimated frequency error signal 216 when the channel undergoes deepfades.

Alternately, a power detector (not shown) may be substituted for themagnitude detector 314 to calculate the average power (i.e., the squaredmagnitude) of the output 309 of the multiplier 308, whereby theinstantaneous power of the output 309 is compared to some fraction ofthe average power. Other variations are also possible.

The present invention controls the adaptation step-size of an adaptiveequalizer. The value of the adaptation step-size μ depends on the rateof channel change (such as a Doppler spread which is related to the WTRUvelocity), and SNR of the channel. For fast channels, it is preferableto use a larger step-size to allow the adaptive equalizer to track thechannel variations quickly. Conversely, for slower channels, a lowerstep-size is desired to reduce the misadjustment error and thus improvethe performance of the adaptive equalizer.

The dependency of the adaptation step-size parameter μ on the SNR issuch that at a high SNR, the value of the adaptation step-size parameterμ tends to be higher, while at a low SNR, the adaptive step-sizeparameter μ is typically small. Additional inputs may also be used asappropriate (e.g., delay spread and the number of active taps in theequalizer filter). The present invention is used to maintain an idealbalance between the convergence speed and accuracy through theestimation of the apparent channel speed.

FIG. 4 is a block diagram of the step-size estimator 136 which includesan apparent channel speed estimator 401. The step-size estimator 136includes an apparent channel speed estimator 401, a step-size mappingunit 440 and an SNR averager 445. The apparent channel speed estimator401 estimates the speed of a channel established between a firsttransceiver which includes the step-size estimator 136, and a secondtransceiver. Equalizer filter tap coefficients 162 are input to theapparent channel speed estimator 401 by the equalizer tap update filter134 shown in FIG. 1. The equalizer filter tap coefficients 162 arecomplex values that are multiplied with an input sample sequence in theequalizer 118. Each of the equalizer filter tap coefficients 162 outputby the equalizer tap update unit 134 is generated by finding the innerproduct of two vectors. One vector is a state (output) of a tapped delayline (TDL) within the equalizer tap update unit 134, and the othervector is the vector of equalizer filter tap coefficients 162 (or aconjugate of them) used by the equalizer tap update unit 134.

Referring to FIG. 4, the apparent channel speed estimator 401 includes atap coefficient extractor 404, an angle calculator 408, a TDL 416, aphase difference function generator 420, an averaging filter 424, anormalizing unit 428, a delay calculator 432 and a speed mapping unit436.

In accordance with the present invention, velocity information isextracted from a history of the filter coefficients used by theequalizer tap update unit 134. This procedure is possible because theequalizer tap update unit 134 adaptively estimates a minimum mean squareerror (MMSE) solution to detect a reference signal such as a pilotsignal. In doing so, the resulting equalizer tap update unit 134 isclose to an inverse of the channel. A speed estimate may be performed bytracking the rate of change of one or more filter tap values used by theequalizer tap update unit 134 which reflect the rate of change of thechannel (i.e., its apparent speed).

The tap coefficient extractor 404 extracts at least one tap coefficientfrom equalizer filter tap coefficients 162 received from the equalizertap update unit 134 and sends the extracted tap coefficient 406 to theangle calculator 408.

A typical channel impulse response can usually be characterized byfinite set of (disjoint) delayed and scaled impulses. The location ofeach of these impulses is referred to as a path (i.e., a component of a“multi-path” channel). The location and the mean power of each of thepaths relative to an FSP determine the location and magnitude of theequalizer tap weights.

The extracted tap coefficient 406 may be a coefficient that correspondsto an FSP, a most significant path (MSP), an average of several taps, orany other paths. The extracted tap coefficient 406 consists of complexnumbers, and thus has an amplitude and a phase (or equivalently, anangle value). The angle calculator 408 outputs the phase 410 of theextracted tap coefficient 406 to both the TDL 416 and the phasedifference function generator 420.

The full length of the TDL 416 may be larger than N (i.e., not alldelays will necessarily have taps). The length of the TDL 416 must be atleast D(N), which corresponds to the tap having the longest delay fromthe input of the TDL 416. The delay from the input of the TDL 416 to theoutput n (0<n<N+1) will be D(n). The TDL 416 shifts data from the inputthrough the next delay element on a first clock tick and on to the nextdelay element on subsequent clock ticks. The TDL 416 operates in asimilar manner as a shift register.

A vector of delays 414, D(k), comprising N delay values D(1) . . . D(N),is input into the TDL 416. The TDL 416 generates N delayed samples 418,X(i−D(k)), k=1 . . . N, in accordance with the vector of delays 414 andthe phase 410 of the extracted tap coefficient 406. The index variable“i” is used as a time index and is suppressed in the sequel.

The phase difference function generator 420 generates N samples of anauto-correlation-like phase difference function based on each of the Ndelayed samples 418 output by the TDL 416 and the phase 410 output bythe angle calculator 408. More specifically, N phase difference functionvalues 422 are generated, one for each element of the vector of delays414. The preferred function is |pi−|phase(1)-phase(n)||, where|x|=absolute value of x, but other such functions can be used.

The averaging filter 424 averages the magnitude of the N phasedifference function values 422 to generate an average phase differencefunction vector 426 having a plurality of elements, avg_phase_dif(k),k=1 . . . N. The averaging filter 424 is essentially a bank of fixedlow-pass filters, such as a moving average filter or a simple infiniteimpulse response (IIR) filter.

The normalizing unit 428 normalizes the elements of the average phasedifference function vector 426 to generate a normalized phase differencefunction vector 430 having a plurality of elements. The measurements arenormalized to a measured function value at a small delay. The firstelement in the average phase difference function vector 426 is used todivide all of the elements of the average phase difference functionvector 426 to complete the normalization process. The first element inthe average phase difference function vector 426 corresponds to thesmallest delay in the TDL 416. It is chosen specifically to have a delaysmall enough such that any phase difference between the phase 410 andthe first element of the N delayed samples 418 are due only to noise andnot due to changes in the channel in order to compensate for randomphase changes due to noise.

For example, the normalization is performed by dividing each element ofthe average phase difference vector 426 with the first element asfollows: norm_phase_dif(k)=avg_phase_dif(k)/avg_phase_dif(1), k=1 . . .N, where avg_phase_dif is the vector of averaged phase differencefunction values.

Each element of the normalized phase difference function vector 430 isthen compared to a threshold by a delay calculator 432 to generate adelay at a threshold. The normalized phase difference function vector430 is a vector of decreasing numbers (at least the first two) startingwith 1.0 that correspond to samples of a curve that is also decreasing(at least near the origin).

The goal of the delay calculator 432 is to estimate the distance (intime/delay) at which the curve crosses the value equal to the threshold.If the threshold is greater than the smallest value in the normalizedphase difference function vector 430, then the estimate is performedusing linear interpolation. If the threshold is less than the smallestvalue in the normalized phase difference function vector 430, then theestimate is performed using linear extrapolation.

The threshold delay 434 is mapped to a speed estimate 438 by the speedmapping unit 436 in accordance with a predefined mapping function. TheSNR averager 445 in the step-size estimator 136 generates a CPICH SNRestimate 446 based on a CPICH_SNR input 139 and sends the CPICH SNRestimate 446 to the step-size mapping unit 440. The speed estimate 438and the CPICH SNR estimate 446 are then mapped by the step-size mappingunit 440 to the step-size, μ, parameter 135 and the filter taps leakagefactor, α, parameter 137 for the equalizer tap update unit 134.

The mapping from speed and SNR is determined empirically. This isimplemented by simulating the performance of the receiver with variousvalues of the step-size, μ (“mu”), parameter 135, and the filter tapsleakage factor, α (“alpha”), parameter 137 for various speeds and SNRs.At each speed and SNR value, the values of μ and α are determined byselecting those values which optimize performance (e.g., lowest BER orhighest throughput). Once the relation between (speed, SNR) and {μ, α}is determined for the simulated points, a more general function can befound by conventional two-dimensional (2-D) curve fitting techniques.Once the equations are established, the mapping procedure may beimplemented by directly implementing the equations (or approximations ofthem), referring to a look up table (LUT), or both.

The filter taps leakage factor, α, is defined as follows:

0<α≦1,  Equation (8)

where α=1 indicates that there is no taps leakage. When it is notdesired to calculate the filter taps leakage factor, α (i.e., it is“optional”), α is just set to 1. Based on the speed estimate 438 and theCPICH SNR estimate 446, the μ parameter 135 and the α parameter 137 areselected.

The adaptation of the filter coefficients in a generic LMS algorithm canbe written as:

{right arrow over (w)} _(n+1) =α·{right arrow over (w)} _(n) +μ·{rightarrow over (e)} _(n),  Equation (9)

where the vector {right arrow over (w)}_(n) represents the current valueof the filter coefficients used by the equalizer tap update unit 134,{right arrow over (w)}_(n+1) represents the new value of the filtercoefficients used by the equalizer tap update unit 134, and the vector{right arrow over (e)}_(n) represents the error signal that is generatedas part of the LMS algorithm of the equalizer tap update unit 134. Theequalizer tap update unit 134 generates the equalizer filter tapcoefficients 162, each of which is a vector signal with L elements,where L is equal to the number of taps.

FIG. 5 is a high-level block diagram depicting the integration of theactive taps mask generator 140 within the NLMS CLE receiver 100. Theequalizer filter 120 includes a delay line (e.g., TDL) 502 and aprocessing unit 506. A frequency corrected sample data stream 116(data_merge_rot) enters the delay line 502 of the equalizer filter 120.Sampling the data in the delay line 502 at the desired sampling ratecreates a data vector 504, (data_vec). The processing unit 506 is usedto calculate the inner product between the output (data_vec) 504 of thedelay line 502 and either one of the (unmasked) equalizer filter tapcoefficients 162, {right arrow over (w)}_(n), generated by the equalizertap update unit 134, or the active taps mask vector 160 generated by theactive taps mask generator 140 (act_taps×{right arrow over (w)}_(n)).

FIG. 5 shows that the equalizer filter 120 outputs a masked equalizeroutput signal 144 and an unmasked equalizer output signal 146. Themasked equalizer output signal 144 is a chip rate signal that ismultiplied with a scrambling code conjugate (scrambling_code_conj)signal 150 via the multiplier 124 to generate a descrambled maskedequalizer output signal 152 (i.e., an estimate of the unscrambledtransmitted chips) which is fed to a first input of the switch 147. Theunmasked equalizer output signal 146 is multiplied with the scramblingcode conjugate signal 150 via the multiplier 123 to generate adescrambled unmasked equalizer output signal 154 which is fed to asecond input of the switch 147.

When an active mask algorithm is running in the active taps maskgenerator 140, the descrambled unmasked equalizer output signal 154 isused as the selected output signal 166 so that all taps are updated asif there was no mask. Thus, the active taps algorithm can examine alltaps as they are updated such that it can be determined which tapsshould be masked or unmasked. When the active mask algorithm is inactive(e.g., in a hold state), then it is preferred to use the signalcorresponding to the masked output of the equalizer such that the errorsignal 174 reflects only the active taps. The masking mode signal 164controls the switch 147 such that the descrambled masked equalizeroutput signal 152 is selected as the signal 166 when the active tapsalgorithm of the active taps mask generator 140 is running, and thedescrambled unmasked equalizer output signal 154 is selected as thesignal 166 when the active taps algorithm of the active taps maskgenerator 140 is held.

The unmasked equalizer output signal 146 is a vector-vector innerproduct of the data vector 504 and is represented by a tap updateEquation (10) as follows:

unmasked_eq_out=data_vec*{right arrow over (w)} _(n),  Equation (10)

where data_vec is the data vector 504 generated by the delay line 502,{right arrow over (w)}_(n) is the values of the equalizer filter tapcoefficients 162 generated by the equalizer tap update unit 134 and (*)indicates a vector-vector inner product. The masked equalizer outputsignal 144 is also a vector-vector inner product of the data vector 504and is represented by a tap update Equation (11) as follows:

masked_eq_out=data_vec*(act_taps·{right arrow over (w)} _(n)),  Equation(11)

where act_taps is a vector used to mask the values of the equalizerfilter tap coefficients 162, (*) indicates a vector-vector inner productand (·) indicates a vector-vector element wise product. The mask vectoris used to eliminate or decrease the contribution of taps elements thatare believed to be more detrimental to the quality of the output than ifthey were used. By the equalizer filter 120 generating two separateequalizer output signals 144, 146, the taps may be monitored while theyare not in use.

The active taps mask vector 160 may be generated in several ways. In asimple approach, the magnitudes of the tap weights are compared to athreshold. If the value is greater than the threshold, the correspondingelement in the active taps mask vector 160 is set to 1, otherwise 0. Themask vector elements may also be set to deemphasize certain tapselements rather than turn them off completely. In that case, the activetaps mask vector 160 takes on values that can range anywhere from 0to 1. The value may be changed gradually rather than abruptly.

Additional information 508, such as SNR, Doppler spread or delay spread,may also be used in setting the mask values. For example, if a delayspread is known to be small, the total number of non-zero elements canbe limited.

The threshold values can be fixed or determined by first making atime-average of the tap magnitude(s) (or other distance metric), andusing this information to set the threshold(s). If no hysteresis isdesired, only one threshold is needed. With hysteresis, at least twothresholds are needed, an upper and a lower. When a tap element exceedsthe upper threshold, the corresponding mask element is set to ‘1’ orallowed to increase towards ‘1’. If a tap element goes below the lowerthreshold, the corresponding mask element is set to ‘0’ or allowed todecrease towards ‘0’.

The threshold values can also be influenced by additional information,such as Doppler spread. For example, if the Doppler spread is known tobe large, the adaptive equalizer will have larger tracking andmisadjustment errors and so it may be desirable to raise thethreshold(s).

The active taps mask generator 140 is controlled by an enable/disableparameter used to set the masking mode signal 164. The active taps maskgenerator 140 controls the number and position of active taps in theequalizer filter 120 when in either the static filter tap masking modeor the dynamic filter tap masking mode. In the static filter tap maskingmode, a fixed mask vector is generated and used to mask the taps (i.e.,zero the taps) accordingly. In the dynamic filter tap masking mode, themasked equalizer output signal 144 is used for generating the equalizerfilter tap coefficients 162. In the dynamic filter tap masking mode, adynamic mask vector is generated and used to mask the taps. In thestatic filter tap masking mode, the unmasked equalizer output signal 146is used for generating the equalizer filter tap coefficients 162.

Referring to FIG. 5, the selection between the static and dynamic filtertap masking modes is determined by the position of a switch 147, whichis controlled by the masking mode signal 164. As previously describedwith respect to FIG. 1, when the masking mode signal 164 indicates thatthe static filter tap masking mode is being used, the switch 147 selectsthe descrambled masked equalizer output signal 152 as a selected outputsignal 166 to be fed to the adder 130. When the masking mode signal 164indicates that the dynamic filter tap masking mode is being used, theswitch 147 selects the descrambled unmasked equalizer output signal 154as the selected output signal 166. In the dynamic filter tap maskingmode, the filter taps are monitored and taps to be masked are selected,whereby the active taps mask vector 160 is generated accordingly by theactive taps mask generator 140.

FIG. 6 is a block diagram of an exemplary active taps mask generator 140in accordance with the present invention. The equalizer filter tapcoefficients 162 generated by the equalizer tap update unit 134 areinput to the active taps mask generator 140. The absolute value (or someother distance measure) is computed on each of (or a subset of) theelements of the equalizer filter tap coefficients 162 by an absolutevalue calculator 602. The absolute value calculator 602 outputs a vectorof tap absolute values (ABS) 604. Averaging is performed by an averagingfilter 606 on each of the elements of the vector of tap ABS 604 togenerate the vector of tap averages 608.

An upper threshold (UT) 612 and a lower threshold (LT) 614 are generatedby a threshold unit 610 based on the vector of tap averages 608 (the UTand LT, respectively). The UT 612 and the LT 614 may be set as afraction (i.e., a percentage) of the average of all elements in thevector of tap averages 608, as a fraction of the largest element(s) orsome other function. Additional optional information 607, (such asstep-size, Doppler spread or SNR), may be used for setting at least oneof the LT and the UT.

The UT 612 is fed to a first mask vector generator 620 and the LT 614 isfed to a second mask vector generator 624. The vector of tap ABS 604 isalso fed to the first mask vector generator 620 and the second maskvector generator 624.

A mask vector stored in a memory 626 becomes a begin mask vector 632 foractive taps estimation. A vector initializer 628 generates all ‘1’svector 630 in the same length as the equalizer filter tap coefficients162 to be stored in the memory 626. The begin mask vector 632 isforwarded from the memory 626 to the first mask vector generator 620directly or alternatively as a trimmed mask vector 618 after beingtrimmed by a vector trimmer 616.

The elements of the begin mask vector 632 may be zeroed by the vectortrimmer 616 at one or both ends in accordance with additionalinformation 615 (such as channel estimation or channel delay spread).For example, if the channel delay spread is small, the begin mask vector632 may be trimmed by zeroing out one or both ends of the begin maskvector 632.

The first mask vector generator 620 sets an element in the begin maskvector 632 (or alternatively, the trimmed mask vector 618) to ‘1’ if thecorresponding element in the vector of tap ABS 604 is above the UT. Thefirst mask vector generator 620 then outputs an intermediate mask vector622.

Still referring to FIG. 6, a second mask vector generator 624 sets anelement in the intermediate mask vector 622 to ‘0’ if the correspondingelement in the vector of tap ABS 604 is below the LT 614 to generate anactive taps mask vector 625. The active taps mask vector 625 is storedin the memory 626 for next iteration. A latch 650, controlled by themasking mode signal 164, determines whether the static or dynamic filtertap masking mode is to be used such that a mask M is made available tothe equalizer filter 120 and the vector norm square estimator 132. Whenthe masking mode signal 164 indicates that the static filter tap maskingmode is to be implemented, the latch 650 latches (i.e., holds) thevalues of the active taps mask vector 625 constant at the value it hadat the time when the masking mode becomes static. When the masking modesignal 164 indicates that the dynamic filter tap masking mode is to beimplemented, the active taps mask vector 625 is passed through the latch650 to provide active taps mask vector signal 160 to the equalizerfilter 120 and the vector norm square estimator 132.

Referring back to FIG. 1, the equalizer filter tap coefficients 162 arederived by comparing the selected output signal 166 to the pilotreference signal 172. Since the selected output signal 166 contains aplurality of superimposed components, only one of which corresponds tothe pilot signal, the NLMS algorithm does not directly control theequalizer output power. Thus, several factors contribute to making thefixed-point design requirements of the filter and despreaderimplementation demanding beyond usual issues associated with the fadingchannels. Among these are large possible span of pilot power to totalpower transmission and the large possible span of per-code data power tototal power transmission. The amplitude of the pilot reference signal172 can be set somewhat arbitrarily in a floating-point environment.However, when fixed-point issues are considered, the amplitude settingcan be important. The fixed-point issues arise in the equalizer filteritself and also in the subsequent de-spreaders.

The present invention also provides a means to control the referencesignal amplitude in such a way as to minimize the fixed-pointrequirements of the equalizer filter, de-spreaders, or a combination ofboth. Moreover, the present invention also provides a means to eliminatethe need for a constellation scaling procedure when quadrature amplitudemodulation (QAM) is employed.

The average power at the equalizer filter output depends on the ratio ofthe pilot power to the total transmitted power and the pilot referenceamplitude. As a by-product in the NLMS equalizer, the relationshipbetween the total input power and the locally created pilot power levelsthrough the process may be used to estimate the CPICH energy per chip(Ec) divided by the total input power (Io), Ec/Io, that can be used asthe strength indicator for the serving cell power level. A WTRU thatuses the above-mentioned equalization method does not require additionalhardware, software and complexity to estimate serving cell CPICH SINR.The periodic neighbor cell measurements will be partially simplifiedsince the serving cell CPICH SINR will be available with simple powercalculations. In a normal deployment scenario, the ratio of the dataportion of the signal to the pilot portion of the signal is allowed tovary. Therefore, the full dynamic range of the signal at the output ofthe equalizer filter also varies. Furthermore, in CDMA systems, thedespreaders also have to contend with these variations in addition tothe variations caused by changes in the number of used codes. Thepresent invention provides a means to reduce the dynamic range of thesignal at the equalizer filter output.

The pilot amplitude reference unit 142 in FIG. 1 controls the referencesignal amplitude and therefore the output power of the equalizer filter120 so as to alleviate the fixed-point requirements. In accordance withone embodiment of the present invention, the estimated filter inputpower and tap weights are used to estimate the output power. Theestimated output power is used to adjust the pilot reference amplitudesignal 168 so that the NLMS CLE receiver 100 naturally adjusts the tapweights to bring the power level into the desired range.

FIG. 7 is a high-level block diagram depicting the integration of thepilot amplitude reference unit 142 in the NLMS CLE receiver 100 of FIG.1 to minimize the dynamic range of the equalizer filter 120 inaccordance with the present invention. The pilot amplitude referenceunit 142 receives the vector normalization signal 158 and the equalizerfilter tap coefficients 162. A vector norm square unit 702 performs avector norm square function on the equalizer filter tap coefficients 162and outputs the result to a first input of a multiplier 704. The vectornormalization signal 158 is fed to a second input of the multiplier 704.The multiplier 704 multiplies the output of the vector norm square unitwith the vector normalization signal 158 to generate an equalizer outputpower signal 706 having a value of P_(EQ).

As shown in FIG. 7, the pilot amplitude reference unit 142 is used tocontrol the amplitude of the pilot reference amplitude signal 168 in aclosed loop manner. The value P_(TARGET) of a target power measurementsignal 176 is divided by the value P_(EQ) of the equalizer output powersignal 706 by a divider 708 to generate a quotient result measurementsignal 710 having a value P_(TARGET)/P_(EQ). The quotient resultmeasurement signal 710 is filtered by a loop filter comprising amultiplier 712 and a delay unit 714, whereby the multiplier 712multiplies the output 716 of the delay unit 714 by the quotient resultmeasurement signal 710 to generate the pilot reference amplitude signal168.

The dynamic output range of the equalizer 120 is adjusted based on apower ratio measurement. The pilot amplitude reference unit 142 receivesan equalizer filter output and calculates a ratio of pilot power to thetotal power, PilotPower/TotalPower. The pilot amplitude reference unit142 then generates a pilot reference amplitude signal 168 based on theratio which is multiplied with a scaled pilot (i.e., CPICH)channelization code 170 by a multiplier 128 to generate a pilotreference signal 172. In this way, the dynamic range of the equalizerfilter 120 output is minimized. Referring to FIG. 1, the pilot amplitudereference unit 142 feeds the equalizer tap update unit 134 via themultiplier 128 and adder 130. The equalizer tap update unit 134 thenprovides equalizer filter tap coefficients 162 to the equalizer filter120. If the output power of the equalizer filter 120 increases, it willbe detected by the pilot amplitude reference unit 142 and will respondby decreasing the amplitude of the pilot reference signal 172. This willin turn cause the tap update unit 134 to generate smaller taps and thusreduce the output power of the equalizer filter 120.

FIG. 8 is a high-level block diagram of an exemplary NLMS CLE receiver800 configured in accordance with another embodiment of the presentinvention. The NLMS CLE receiver 800 is a joint processing NLMS receiverwhich uses a single adaptive equalizer filter 120. The NLMS CLE receiver800 includes a plurality of antennas 102A, 102B, a plurality of samplers104A, 104B, a multiplexer 108, a multiplier 114 and an NLMS equalizer818. The NLMS equalizer 818 includes the equalizer filter 120 and a tapcoefficients generator 822.

The NLMS CLE receiver 800 of FIG. 8 is different from the NLMS CLEreceiver 100 of FIG. 1 in that the receiver 800 includes a pilotamplitude reference unit 842 which receives the masked equalizer outputsignal 144 directly from the equalizer filter, rather than receiving thenormalized signal 158 from the vector norm square estimator 132.

FIG. 9 is a detailed block diagram depicting the integration of thepilot amplitude reference unit 842 in the NLMS CLE receiver 800 of FIG.8. Power or other measurements are performed on the masked equalizeroutput signal 144, which is a pilot-trained adaptive equalizer, by apower measurement unit 902 to generate an equalizer output powermeasurement signal 904 having a value of P_(EQ). For example, power ofthe masked equalizer output signal 144 may be estimated in the powermeasurement unit 902 based on the following Equation (12):

P _(EQ)=(1−F _(p))*|x| ² +F _(p) *P _(EQ).  Equation (12)

where x is the amplitude of the masked equalizer output signal 144,P_(EQ) is the value of the equalizer output power measurement signal904, and F_(p) is a filter parameter between 0.0 and 1.0.

As shown in FIG. 9, the pilot amplitude reference unit 842 is used tocontrol the amplitude of the pilot reference amplitude signal 168 in aclosed loop manner. The value P_(TARGET) of a target power measurementsignal 176 is divided by the value P_(EQ) of the equalizer output powermeasurement signal 904 by a divider 906 to generate a quotient resultmeasurement signal 908 having a value P_(TARGET)/P_(EQ). The quotientresult measurement signal 908 is filtered by a loop filter comprising amultiplier 910 and a delay unit 912, whereby the multiplier 910multiplies the output 914 of the delay unit 714 with the quotient resultmeasurement signal 908 to generate the pilot reference amplitude signal168.

With respect to the despreaders, the despreader dynamic range may beoptimized based on measurements. A ratio of pilot power to total powerfor an intended WTRU is estimated. The number of codes used is thenestimated or obtained. The reference amplitude is then adjusted by afactor based on these parameters (e.g.,sqrt(NumCodes*PilotPower/TotalPower)/SF), where SF is the spreadingfactor (i.e., the number of chips used to spread each symbol) andNumCodes is the number of codes used to spread HS-DSCH data intended tobe received by the equalizer receiver. In this way the dynamic range isminimized for the despreaders and (if accurate enough) can eliminate theneed for constellation scaling.

Alternatively, the despreader dynamic range may be optimized based onconstellation scaling feedback. A scaling factor generated byconstellation scaling may be used as feedback to control the referenceamplitude and maintain a specified (e.g., unity power) symbolconstellation.

Although the features and elements of the present invention aredescribed in the preferred embodiments in particular combinations, eachfeature or element can be used alone without the other features andelements, or in various other combinations with or without otherfeatures and elements of the present invention.

1. A method for adaptive equalization comprising: an equalizer filteroutputting a masked equalizer signal and an unmasked equalizer signal;generating equalizer filter tap coefficients; selecting one of themasked or unmasked equalizer signals; generating an error signal;generating an active taps mask; and masking active taps, based on theactive taps mask, associated with the error signal.
 2. The method ofclaim 1 further comprising: a pilot amplitude reference unit generatinga reference amplitude signal used to adjust an output power; generatinga pilot reference signal by multiplying the reference amplitude signalwith a scaled pilot channelization code signal; and generating anequalizer tap update signal by subtracting the signal output by theswitch from the pilot reference signal.
 3. The method of claim 2 whereinthe scaled pilot channelization code signal is a common pilot channel(CPICH) channelization code signal.
 4. The method of claim 2 furthercomprising: descrambling an equalizer tapped delay line (TDL) signalthat is output from the equalizer filter.
 5. The method of claim 4further comprising: generating a normalization signal having a valuebased on the descrambled equalizer TDL signal.
 6. The method of claim 5further comprising: performing a vector norm square function on theequalizer filter tap coefficients; generating a vector norm squareoutput signal based on the performing the vector norm square function;multiplying the vector norm square output signal with the normalizationsignal to generate an equalizer output power signal; and dividing apower target measurement signal by the value of the equalizer outputpower signal to generate a quotient result measurement signal, whereinthe reference amplitude signal is based on the quotient resultmeasurement signal.
 7. The method of claim 1 further comprising:receiving the masked equalizer signal; measuring a power of the maskedequalizer signal; generating an equalizer output power signal; anddividing a power target measurement signal by the value of the equalizeroutput power signal to generate a quotient result measurement signal,wherein the reference amplitude signal is based on the quotient resultmeasurement signal.
 8. The method of claim 7 further comprising:estimating a serving cell signal strength based on the quotient resultmeasurement signal.
 9. The method of claim 7 further comprising:outputting an equalizer tapped delay line (TDL) signal from theequalizer filter; and descrambling the equalizer TDL signal.
 10. Themethod of claim 9 further comprising: generating a normalization signalhaving a value equal to the norm squared of the value of the descrambledTDL signal.